Group Predictions

Row

Win percentage for the week

Season Win Percentage

Games Correct

163

Games Picked

224

Number of predictions

111

Row

This Week’s Predictions
Game Prediction Winner Correct Correct Votes Correct Percent
1 Los Angeles Rams Los Angeles Rams Yes 69 0.6216
2 Baltimore Ravens Baltimore Ravens Yes 109 0.9820
3 Cincinnati Bengals Cincinnati Bengals Yes 107 0.9640
4 Dallas Cowboys Dallas Cowboys Yes 68 0.6126
5 Kansas City Chiefs Kansas City Chiefs Yes 108 0.9730
6 Houston Texans Houston Texans Yes 78 0.7027
7 New York Jets New York Jets Yes 82 0.7387
8 Washington Commanders Washington Commanders Yes 103 0.9279
9 Detroit Lions Buffalo Bills No 28 0.2523
10 Denver Broncos Denver Broncos Yes 107 0.9640
11 Arizona Cardinals Arizona Cardinals Yes 105 0.9459
12 Philadelphia Eagles Philadelphia Eagles Yes 78 0.7027
13 Los Angeles Chargers Tampa Bay Buccaneers No 25 0.2252
14 Green Bay Packers Green Bay Packers Yes 79 0.7117
15 Minnesota Vikings Minnesota Vikings Yes 110 0.9910
16 Atlanta Falcons Atlanta Falcons Yes 99 0.8919

Individual Predictions

row

Individual Table

Individual Results
Week 15
Name Weekly # Correct Percent Weeks Picked Season Percent Adj Season Percent Season Trend
Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Week 15
Zechariah Ziebarth 8 8 8 10 5 10 10 11 11 NA 12 8 15 10 16 1.0000 14 0.6762 0.6311
Nicholas Nguyen 11 8 5 8 7 12 11 9 10 9 10 9 NA 9 16 1.0000 14 0.6442 0.6013
Matthew Blair NA NA NA NA NA 11 10 12 10 9 11 8 15 11 15 0.9375 10 0.7671 0.5114
Erik Neumann 12 8 9 9 7 13 10 11 12 9 10 NA NA 10 15 0.9375 13 0.6923 0.6000
Jonathan Smith 11 NA 4 10 7 NA 8 11 10 7 9 7 15 9 15 0.9375 13 0.6340 0.5495
Michael Linder 11 9 9 NA NA 12 10 11 10 NA 11 NA 13 NA 14 0.8750 10 0.7143 0.4762
Bradley Hobson 13 7 8 11 7 13 10 10 11 8 NA 10 16 11 14 0.8750 14 0.7095 0.6622
Jeremy Mounce 12 8 8 NA 10 12 NA NA NA 10 10 NA NA NA 14 0.8750 8 0.7000 0.3733
Nicholas Cinco 12 8 NA NA 6 11 11 12 11 11 8 9 12 8 14 0.8750 13 0.6927 0.6003
Thomas Brenstuhl 9 8 NA 6 7 9 10 13 11 9 13 9 15 11 14 0.8750 14 0.6923 0.6461
Jeremy Stieler 11 9 6 11 6 13 11 11 11 9 9 8 16 10 14 0.8750 15 0.6920 0.6920
Heather Ellenberger 13 8 7 8 7 12 11 11 13 9 9 10 14 9 14 0.8750 15 0.6920 0.6920
Brayant Rivera 10 8 9 8 6 13 11 10 12 9 11 10 13 10 14 0.8750 15 0.6875 0.6875
Shawn Carden 10 9 10 10 8 11 10 11 11 9 8 8 15 9 14 0.8750 15 0.6830 0.6830
Karen Coleman 13 6 NA 11 9 9 10 9 11 8 9 9 14 10 14 0.8750 14 0.6827 0.6372
Kevin Kehoe 13 7 9 10 8 13 12 11 9 8 8 8 13 9 14 0.8750 15 0.6786 0.6786
Pablo Burgosramos 9 5 8 9 5 14 12 12 12 7 11 10 13 10 14 0.8750 15 0.6741 0.6741
Edward Ford 9 7 6 10 5 10 10 13 11 9 12 10 14 11 14 0.8750 15 0.6741 0.6741
Scott Lefton 10 8 8 7 7 11 11 10 11 10 10 8 15 10 14 0.8750 15 0.6696 0.6696
Karen Richardson 10 9 7 9 11 8 8 12 8 10 9 9 13 10 14 0.8750 15 0.6562 0.6562
Paul Seitz 11 9 9 NA 8 10 11 NA NA 7 8 8 14 7 14 0.8750 12 0.6554 0.5243
Darvin Graham 12 7 6 9 8 11 9 NA 10 9 9 10 NA 11 14 0.8750 13 0.6510 0.5642
Noah Gosswiller 8 7 NA 10 8 NA 10 11 10 10 10 8 NA 9 14 0.8750 12 0.6461 0.5169
Kristen White 14 7 9 9 8 9 9 8 8 9 9 8 13 8 14 0.8750 15 0.6339 0.6339
Brittany Pillar NA NA NA NA NA 10 12 NA NA 10 11 10 16 11 13 0.8125 8 0.8087 0.4313
Robert Cunningham 14 9 10 12 8 12 11 11 12 9 11 10 NA 10 13 0.8125 14 0.7308 0.6821
Marc Agne 14 7 9 13 6 13 10 9 12 10 10 7 16 9 13 0.8125 15 0.7054 0.7054
Patrick Tynan 12 8 7 9 8 12 NA 12 12 10 10 6 16 11 13 0.8125 14 0.6986 0.6520
Gregory Brown 15 7 6 9 8 12 9 9 13 9 10 9 15 12 13 0.8125 15 0.6964 0.6964
William Schouviller 12 7 9 9 11 13 10 9 NA 7 11 8 15 11 13 0.8125 14 0.6938 0.6475
Anthony Bloss 13 8 8 11 8 13 11 11 9 7 10 9 14 9 13 0.8125 15 0.6875 0.6875
Kevin Buettner 12 8 8 10 7 11 10 9 10 10 10 8 16 10 13 0.8125 15 0.6786 0.6786
Nicole Dike 13 7 8 10 7 10 10 12 10 9 10 8 15 9 13 0.8125 15 0.6741 0.6741
Stephen Bush 9 7 4 10 9 13 13 9 10 10 8 9 16 10 13 0.8125 15 0.6696 0.6696
Ryan Cvik 10 8 9 11 9 11 11 13 10 7 7 9 12 10 13 0.8125 15 0.6696 0.6696
Jason Schattel 13 7 6 9 10 11 9 10 11 9 10 10 13 9 13 0.8125 15 0.6696 0.6696
Clevante Granville 9 11 NA NA 5 11 11 9 10 11 9 10 NA 8 13 0.8125 12 0.6648 0.5318
Michael Moore 11 6 7 7 8 12 NA 9 9 NA 12 9 16 10 13 0.8125 13 0.6615 0.5733
Jennifer Arty 10 7 9 7 7 12 8 12 11 9 10 8 15 9 13 0.8125 15 0.6562 0.6562
Antonio Mitchell 11 7 8 9 9 11 10 11 10 8 8 9 13 9 13 0.8125 15 0.6518 0.6518
Cade Martinez 10 7 8 8 6 11 11 9 10 10 9 8 15 11 13 0.8125 15 0.6518 0.6518
Montee Brown 10 6 8 7 8 14 11 10 8 10 10 9 14 8 13 0.8125 15 0.6518 0.6518
Michael Branson 9 8 8 9 8 11 9 11 10 9 11 7 14 9 13 0.8125 15 0.6518 0.6518
Brian Patterson 11 6 9 9 6 NA 9 13 NA 9 12 7 14 9 13 0.8125 13 0.6513 0.5645
Jonathon Leslein 10 8 7 10 8 12 10 10 8 10 7 10 12 10 13 0.8125 15 0.6473 0.6473
James Small 12 NA 9 10 8 10 9 9 10 10 NA 6 11 8 13 0.8125 13 0.6443 0.5584
Joshua Tracey 12 5 8 6 7 NA 9 13 10 7 10 9 16 9 13 0.8125 14 0.6381 0.5956
Jason Jackson 12 7 5 6 5 12 9 11 10 10 12 9 NA 10 13 0.8125 14 0.6298 0.5878
Melissa Printup 8 9 9 6 10 10 10 10 7 10 9 8 13 7 13 0.8125 15 0.6205 0.6205
Marcus Evans 11 8 NA 8 7 10 7 9 10 6 11 8 12 8 13 0.8125 14 0.6154 0.5744
Ronald Schmidt 10 10 5 9 6 8 12 10 NA 7 9 10 NA 8 13 0.8125 13 0.6062 0.5254
Kyle May 10 8 5 6 8 NA 12 10 9 8 8 8 13 7 13 0.8125 14 0.5952 0.5555
Robert Lynch 6 9 8 6 9 7 7 12 NA 9 7 8 15 NA 13 0.8125 13 0.5918 0.5129
Aubrey Conn 13 7 10 9 8 12 12 9 13 9 10 11 16 10 12 0.7500 15 0.7188 0.7188
Michael Pacifico 13 8 7 9 9 12 12 10 14 9 11 10 14 10 12 0.7500 15 0.7143 0.7143
Nathan Brown 13 8 9 11 9 NA 10 11 14 9 10 10 14 10 12 0.7500 14 0.7143 0.6667
Bruce Williams 13 9 10 8 9 13 12 10 NA 10 10 9 14 9 12 0.7500 14 0.7081 0.6609
Chester Todd 13 8 8 8 9 13 13 10 9 9 11 9 15 10 12 0.7500 15 0.7009 0.7009
Brian Hollmann NA NA NA 8 8 10 10 11 9 8 12 8 16 11 12 0.7500 12 0.6989 0.5591
Randy Dick 11 7 8 8 9 14 10 10 13 11 10 9 14 10 12 0.7500 15 0.6964 0.6964
David Dupree 13 8 10 9 7 11 11 11 12 NA 9 8 13 10 12 0.7500 14 0.6857 0.6400
Shaun Dahl 14 7 9 11 10 10 10 8 9 11 8 11 12 11 12 0.7500 15 0.6830 0.6830
George Sweet 13 9 6 10 11 9 11 11 12 7 10 11 12 9 12 0.7500 15 0.6830 0.6830
Daniel Baller 14 6 9 8 7 9 10 12 10 10 10 10 15 10 12 0.7500 15 0.6786 0.6786
Daniel Halse 12 6 8 10 7 13 9 11 11 11 NA 8 14 9 12 0.7500 14 0.6714 0.6266
Richard Beeghley 11 7 6 11 7 14 10 10 10 8 9 9 14 11 12 0.7500 15 0.6652 0.6652
David Plate 10 8 8 8 9 NA NA NA 13 10 8 9 14 NA 12 0.7500 11 0.6566 0.4815
Michael Moss 13 8 8 8 10 13 8 9 11 9 10 6 11 10 12 0.7500 15 0.6518 0.6518
Trevor Macgavin 12 7 10 8 8 8 9 7 10 7 11 10 16 9 12 0.7500 15 0.6429 0.6429
Christopher Mulcahy 11 9 7 8 NA 8 9 9 10 9 10 9 13 11 12 0.7500 14 0.6429 0.6000
Ramar Williams 10 8 7 11 8 11 11 10 8 8 9 8 13 10 12 0.7500 15 0.6429 0.6429
Brandon Parks 12 6 9 9 6 13 NA NA 12 10 10 6 NA 8 12 0.7500 12 0.6384 0.5107
Matthew Olguin 10 8 9 9 7 12 11 11 9 7 8 5 14 9 12 0.7500 15 0.6295 0.6295
Yiming Hu 12 NA 7 7 6 8 12 9 NA 9 12 8 11 NA 12 0.7500 12 0.6278 0.5022
Anthony Rockemore 13 8 6 8 7 NA 8 NA NA 9 9 9 12 10 12 0.7500 12 0.6201 0.4961
Bunnaro Sun 12 5 8 11 6 8 9 9 12 8 8 NA 14 NA 12 0.7500 13 0.6162 0.5340
Megan Fitzgerald 8 11 9 10 NA NA 8 10 NA NA NA 7 13 4 12 0.7500 10 0.6013 0.4009
Steven Webster 7 7 9 6 7 9 NA 11 NA 8 10 8 NA 8 12 0.7500 12 0.5730 0.4584
Randolph Tidd 11 7 8 12 NA 12 11 12 13 9 11 7 16 10 11 0.6875 14 0.7143 0.6667
Robert Gelo 14 8 9 9 8 13 13 11 12 10 10 9 12 11 11 0.6875 15 0.7143 0.7143
Christopher Sims 11 9 10 8 7 10 12 14 11 9 8 9 16 10 11 0.6875 15 0.6920 0.6920
Chris Papageorge 14 8 10 11 8 12 12 12 11 8 9 7 15 7 11 0.6875 15 0.6920 0.6920
Matthew Schultz 13 10 9 8 9 9 9 12 11 8 11 10 14 9 11 0.6875 15 0.6830 0.6830
Jennifer Bouland 13 8 10 7 8 11 10 11 9 9 12 10 NA 11 11 0.6875 14 0.6731 0.6282
Jeffrey Rudderforth 11 11 10 9 6 7 10 11 12 9 8 8 14 9 11 0.6875 15 0.6518 0.6518
Thomas Mccoy 10 7 6 8 9 11 11 10 12 10 10 8 13 9 11 0.6875 15 0.6473 0.6473
Jared Kaanga 11 9 9 8 7 10 9 11 13 9 9 7 NA 11 11 0.6875 14 0.6442 0.6013
Jeffrey Zornes 9 11 6 8 7 10 9 11 9 10 NA 8 15 11 11 0.6875 14 0.6429 0.6000
Steward Hogans 10 7 10 NA NA NA NA 10 13 8 8 9 11 8 11 0.6875 11 0.6364 0.4667
Amy Asberry 11 8 6 10 NA 12 9 NA 9 8 NA NA 13 9 11 0.6875 11 0.6347 0.4654
Rachel Follo 15 8 6 6 9 7 10 11 9 9 10 7 13 11 11 0.6875 15 0.6339 0.6339
Diance Durand 9 9 12 7 8 10 9 11 11 7 9 8 12 NA 11 0.6875 14 0.6303 0.5883
Vincent Scannelli 11 7 7 11 8 8 11 12 9 8 9 7 12 NA 11 0.6875 14 0.6209 0.5795
Terry Hardison 13 8 6 7 4 11 10 12 11 9 11 7 NA NA 11 0.6875 13 0.6154 0.5333
Gary Lawrence 10 6 5 5 7 9 9 10 9 9 11 8 16 11 11 0.6875 15 0.6071 0.6071
Robert Martin 7 NA 9 8 8 8 7 NA 8 7 7 9 11 8 11 0.6875 13 0.5625 0.4875
Keven Talbert 10 7 9 11 9 14 13 9 9 11 10 10 12 10 10 0.6250 15 0.6875 0.6875
Pamela Augustine 14 9 9 NA 7 11 9 NA 10 NA 10 9 14 9 10 0.6250 12 0.6798 0.5438
Bryson Scott 10 9 7 NA 7 12 11 12 10 9 NA 9 15 10 10 0.6250 13 0.6753 0.5853
George Brown 14 7 8 7 6 11 10 12 9 12 11 8 12 11 10 0.6250 15 0.6607 0.6607
Earl Dixon 10 9 6 9 9 NA 11 10 9 9 10 10 15 11 10 0.6250 14 0.6571 0.6133
Cheryl Brown 11 6 9 8 8 10 NA 9 8 10 11 9 14 10 10 0.6250 14 0.6364 0.5940
Louie Renew 9 8 12 4 10 8 8 11 11 8 10 9 NA 10 10 0.6250 14 0.6154 0.5744
Ryan Shipley 11 6 10 8 5 9 11 NA 10 NA 9 10 NA NA 10 0.6250 11 0.6000 0.4400
Daniel Major 8 10 11 6 8 11 NA 10 10 10 11 9 14 11 9 0.5625 14 0.6603 0.6163
Walter Archambo 8 8 7 9 6 12 11 11 12 10 10 9 15 9 9 0.5625 15 0.6518 0.6518
David Humes 10 9 8 11 5 8 12 8 12 11 11 6 14 9 9 0.5625 15 0.6384 0.6384
Jack Wheeler 9 6 5 10 8 NA 9 9 10 8 10 7 15 11 9 0.5625 14 0.6000 0.5600
Richard Conkle 7 6 6 8 7 10 12 11 9 NA 8 NA NA 10 9 0.5625 12 0.5691 0.4553
Andrew Gray 5 8 9 7 NA NA 7 9 7 11 8 6 5 10 9 0.5625 13 0.5153 0.4466
Akilah Gamble 9 NA 12 9 6 8 12 6 NA 8 NA 7 NA NA 3 0.1875 10 0.5333 0.3555
Clayton Grimes 14 NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0000 1 0.8750 0.0583
Tanaysa Henderson NA NA NA NA NA 12 NA NA NA NA NA NA NA NA NA 0.0000 1 0.8571 0.0571
Wallace Savage 12 NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0000 1 0.7500 0.0500
Brian Holder 12 NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0000 1 0.7500 0.0500
Sandra Carter 12 NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0000 1 0.7500 0.0500
Ryan Wiggins NA NA NA NA NA NA NA NA 11 NA NA NA NA NA NA 0.0000 1 0.7333 0.0489
Steven Maisonneuve NA NA NA NA 11 10 11 12 11 8 NA 10 12 9 NA 0.0000 9 0.7231 0.4339
Heather Kohler 12 NA 7 12 9 11 NA 12 NA 8 9 10 14 NA NA 0.0000 10 0.6980 0.4653
Jeremy Krammes 12 NA NA NA NA NA NA 10 NA 10 NA NA NA NA NA 0.0000 3 0.6957 0.1391
Terrence Lee 11 NA NA NA NA NA NA NA NA NA NA 9 NA NA NA 0.0000 2 0.6897 0.0920
Daniel Gray 11 NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0000 1 0.6875 0.0458
Philip Driskill 12 7 8 10 8 NA 13 11 10 NA NA 9 14 10 NA 0.0000 11 0.6747 0.4948
Robert Sokol 10 8 NA NA 6 9 9 13 12 9 10 8 14 10 NA 0.0000 12 0.6705 0.5364
Rafael Torres 12 9 8 7 8 10 12 10 12 11 11 7 12 10 NA 0.0000 14 0.6683 0.6237
Darryle Sellers 11 11 6 8 9 11 9 10 12 9 12 7 15 9 NA 0.0000 14 0.6683 0.6237
Travis Delagardelle 11 12 10 8 6 11 12 11 11 9 9 NA NA NA NA 0.0000 11 0.6627 0.4860
George Mancini 11 8 6 NA 8 6 12 NA 11 9 11 10 13 11 NA 0.0000 12 0.6591 0.5273
Kamar Morgan 12 6 8 5 8 12 9 12 10 8 10 10 16 10 NA 0.0000 14 0.6538 0.6102
Nahir Shepard 11 8 10 8 6 12 8 12 9 9 11 10 13 9 NA 0.0000 14 0.6538 0.6102
Anthony Brinson 11 7 NA 9 10 11 9 12 6 NA NA 8 14 10 NA 0.0000 11 0.6524 0.4784
George Hall 12 NA 8 NA NA NA NA NA NA 10 NA NA NA NA NA 0.0000 3 0.6522 0.1304
Michelle Fraterrigo 11 8 9 9 7 11 12 12 11 8 8 10 NA 9 NA 0.0000 13 0.6510 0.5642
Ryan Baum 14 4 9 10 9 NA 10 10 11 10 NA 8 14 8 NA 0.0000 12 0.6500 0.5200
Paul Presti 12 8 9 12 7 11 8 10 NA 10 9 NA NA NA NA 0.0000 10 0.6358 0.4239
Tara Bridgett 11 8 8 8 NA 9 NA 10 10 NA NA NA 15 NA NA 0.0000 8 0.6320 0.3371
Keisha Vasquez 8 7 9 9 11 11 9 12 8 8 NA NA 14 NA NA 0.0000 11 0.6310 0.4627
Jordan Forwood 11 8 6 11 NA 13 NA 10 NA NA NA NA NA NA NA 0.0000 6 0.6277 0.2511
Kenneth Nielsen 13 8 7 NA 8 9 11 10 NA NA NA NA 11 NA NA 0.0000 8 0.6260 0.3339
Desmond Jenkins 10 7 7 NA 7 12 8 NA NA NA NA NA 15 NA NA 0.0000 7 0.6168 0.2878
Wayne Schofield 7 5 9 5 7 7 11 11 10 8 13 10 13 11 NA 0.0000 14 0.6106 0.5699
David Hadley 13 10 8 NA 8 NA 8 NA NA NA NA NA NA NA NA 0.0000 5 0.6104 0.2035
Jose Torres Mendoza 12 8 8 8 NA NA 8 9 10 11 8 7 NA 10 NA 0.0000 11 0.6037 0.4427
Wayne Gokey 13 7 NA 11 NA NA 8 NA 8 NA NA NA NA NA NA 0.0000 5 0.6026 0.2009
Jonathan Knight 13 10 9 6 7 NA 11 NA NA NA NA NA NA NA NA 0.0000 6 0.6022 0.2409
Kevin Green 11 9 NA 8 7 12 NA 8 NA NA NA NA NA NA NA 0.0000 6 0.5978 0.2391
Derrick Elam 13 9 8 11 7 10 8 9 NA NA NA 7 NA 7 NA 0.0000 10 0.5973 0.3982
Jeffrey Dusza 11 8 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0000 2 0.5938 0.0792
Cherylynn Vidal 13 9 8 8 NA NA NA NA NA NA NA NA NA NA NA 0.0000 4 0.5938 0.1583
Adam Konkle 10 9 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0000 2 0.5938 0.0792
Thomas Cho 10 6 NA 11 7 12 NA 8 9 NA NA NA NA NA NA 0.0000 7 0.5888 0.2748
Jason Miranda 10 7 8 NA 9 11 8 NA NA NA NA NA NA NA NA 0.0000 6 0.5824 0.2330
Jennifer Wilson 11 9 10 6 NA 7 11 NA NA NA NA NA NA NA NA 0.0000 6 0.5806 0.2322
Sheryl Claiborne-Smith 11 7 NA NA NA 7 7 10 7 7 9 9 10 9 NA 0.0000 11 0.5741 0.4210
Joseph Martin 10 7 8 8 8 10 9 NA NA NA NA NA NA NA NA 0.0000 7 0.5607 0.2617
Min Choi 10 NA 7 NA 8 7 NA 10 NA NA NA NA NA NA NA 0.0000 5 0.5526 0.1842
Lawrence Thuotte 9 5 12 NA 8 NA NA NA NA NA NA NA NA NA NA 0.0000 4 0.5484 0.1462
Donald Park 9 NA 6 NA NA 10 NA NA NA NA NA NA NA NA NA 0.0000 3 0.5435 0.1087
Gabriel Quinones 10 7 6 9 NA 11 8 7 NA NA NA 6 NA 9 NA 0.0000 9 0.5407 0.3244
Monte Henderson 9 8 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0000 2 0.5312 0.0708
David Kim 9 8 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0000 2 0.5312 0.0708
Jamie Ainsleigh-Wong 9 8 9 9 8 5 NA NA NA NA NA NA NA NA NA 0.0000 6 0.5217 0.2087
Jay Kelly 10 9 7 7 5 10 7 NA NA NA NA NA NA NA NA 0.0000 7 0.5140 0.2399
Zachary Brosemer 8 NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0000 1 0.5000 0.0333
Antonio Chapa 8 NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0000 1 0.5000 0.0333
Vincent Kandian 9 8 8 7 NA NA NA NA NA NA NA NA NA NA NA 0.0000 4 0.5000 0.1333
Ashley Johnson 9 NA 6 NA 6 NA NA NA NA NA 5 9 8 NA NA 0.0000 6 0.4831 0.1932
Ashlyn Dortch 9 NA NA 8 NA 5 9 6 NA NA NA NA NA NA NA 0.0000 5 0.4805 0.1602
Gabrieal Feiling 10 NA 5 NA NA NA NA NA NA NA NA NA NA NA NA 0.0000 2 0.4688 0.0625
Jasprin Smith 6 NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0000 1 0.3750 0.0250
Robert Epps NA 6 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0000 1 0.3750 0.0250

Season Leaderboard

Season Leaderboard (Season Percent)
Week 15
Season Rank Name Donuts Won Weeks Picked Season Percent Adj Season Percent Season Trend
1 Clayton Grimes 0 1 0.8750 0.0583
2 Tanaysa Henderson 0 1 0.8571 0.0571
3 Brittany Pillar 1 8 0.8087 0.4313
4 Matthew Blair 0 10 0.7671 0.5114
5 Brian Holder 0 1 0.7500 0.0500
5 Sandra Carter 0 1 0.7500 0.0500
5 Wallace Savage 0 1 0.7500 0.0500
8 Ryan Wiggins 0 1 0.7333 0.0489
9 Robert Cunningham 0 14 0.7308 0.6821
10 Steven Maisonneuve 1 9 0.7231 0.4339
11 Aubrey Conn 2 15 0.7188 0.7188
12 Michael Linder 0 10 0.7143 0.4762
12 Michael Pacifico 1 15 0.7143 0.7143
12 Nathan Brown 1 14 0.7143 0.6667
12 Randolph Tidd 1 14 0.7143 0.6667
12 Robert Gelo 1 15 0.7143 0.7143
17 Bradley Hobson 1 14 0.7095 0.6622
18 Bruce Williams 0 14 0.7081 0.6609
19 Marc Agne 2 15 0.7054 0.7054
20 Chester Todd 1 15 0.7009 0.7009
21 Jeremy Mounce 0 8 0.7000 0.3733
22 Brian Hollmann 1 12 0.6989 0.5591
23 Patrick Tynan 1 14 0.6986 0.6520
24 Heather Kohler 0 10 0.6980 0.4653
25 Gregory Brown 2 15 0.6964 0.6964
25 Randy Dick 1 15 0.6964 0.6964
27 Jeremy Krammes 0 3 0.6957 0.1391
28 William Schouviller 1 14 0.6938 0.6475
29 Nicholas Cinco 0 13 0.6927 0.6003
30 Erik Neumann 0 13 0.6923 0.6000
30 Thomas Brenstuhl 1 14 0.6923 0.6461
32 Chris Papageorge 0 15 0.6920 0.6920
32 Christopher Sims 2 15 0.6920 0.6920
32 Heather Ellenberger 0 15 0.6920 0.6920
32 Jeremy Stieler 1 15 0.6920 0.6920
36 Terrence Lee 0 2 0.6897 0.0920
37 Anthony Bloss 0 15 0.6875 0.6875
37 Brayant Rivera 0 15 0.6875 0.6875
37 Daniel Gray 0 1 0.6875 0.0458
37 Keven Talbert 2 15 0.6875 0.6875
41 David Dupree 0 14 0.6857 0.6400
42 George Sweet 2 15 0.6830 0.6830
42 Matthew Schultz 0 15 0.6830 0.6830
42 Shaun Dahl 1 15 0.6830 0.6830
42 Shawn Carden 0 15 0.6830 0.6830
46 Karen Coleman 0 14 0.6827 0.6372
47 Pamela Augustine 0 12 0.6798 0.5438
48 Daniel Baller 0 15 0.6786 0.6786
48 Kevin Buettner 1 15 0.6786 0.6786
48 Kevin Kehoe 0 15 0.6786 0.6786
51 Zechariah Ziebarth 1 14 0.6762 0.6311
52 Bryson Scott 0 13 0.6753 0.5853
53 Philip Driskill 1 11 0.6747 0.4948
54 Edward Ford 0 15 0.6741 0.6741
54 Nicole Dike 0 15 0.6741 0.6741
54 Pablo Burgosramos 1 15 0.6741 0.6741
57 Jennifer Bouland 0 14 0.6731 0.6282
58 Daniel Halse 0 14 0.6714 0.6266
59 Robert Sokol 0 12 0.6705 0.5364
60 Jason Schattel 0 15 0.6696 0.6696
60 Ryan Cvik 0 15 0.6696 0.6696
60 Scott Lefton 0 15 0.6696 0.6696
60 Stephen Bush 2 15 0.6696 0.6696
64 Darryle Sellers 0 14 0.6683 0.6237
64 Rafael Torres 0 14 0.6683 0.6237
66 Richard Beeghley 1 15 0.6652 0.6652
67 Clevante Granville 0 12 0.6648 0.5318
68 Travis Delagardelle 1 11 0.6627 0.4860
69 Michael Moore 1 13 0.6615 0.5733
70 George Brown 1 15 0.6607 0.6607
71 Daniel Major 0 14 0.6603 0.6163
72 George Mancini 0 12 0.6591 0.5273
73 Earl Dixon 0 14 0.6571 0.6133
74 David Plate 0 11 0.6566 0.4815
75 Jennifer Arty 0 15 0.6562 0.6562
75 Karen Richardson 1 15 0.6562 0.6562
77 Paul Seitz 0 12 0.6554 0.5243
78 Kamar Morgan 1 14 0.6538 0.6102
78 Nahir Shepard 0 14 0.6538 0.6102
80 Anthony Brinson 0 11 0.6524 0.4784
81 George Hall 0 3 0.6522 0.1304
82 Antonio Mitchell 0 15 0.6518 0.6518
82 Cade Martinez 0 15 0.6518 0.6518
82 Jeffrey Rudderforth 0 15 0.6518 0.6518
82 Michael Branson 0 15 0.6518 0.6518
82 Michael Moss 0 15 0.6518 0.6518
82 Montee Brown 1 15 0.6518 0.6518
82 Walter Archambo 0 15 0.6518 0.6518
89 Brian Patterson 0 13 0.6513 0.5645
90 Darvin Graham 0 13 0.6510 0.5642
90 Michelle Fraterrigo 0 13 0.6510 0.5642
92 Ryan Baum 0 12 0.6500 0.5200
93 Jonathon Leslein 0 15 0.6473 0.6473
93 Thomas Mccoy 0 15 0.6473 0.6473
95 Noah Gosswiller 0 12 0.6461 0.5169
96 James Small 0 13 0.6443 0.5584
97 Jared Kaanga 0 14 0.6442 0.6013
97 Nicholas Nguyen 1 14 0.6442 0.6013
99 Christopher Mulcahy 0 14 0.6429 0.6000
99 Jeffrey Zornes 0 14 0.6429 0.6000
99 Ramar Williams 0 15 0.6429 0.6429
99 Trevor Macgavin 1 15 0.6429 0.6429
103 Brandon Parks 0 12 0.6384 0.5107
103 David Humes 0 15 0.6384 0.6384
105 Joshua Tracey 1 14 0.6381 0.5956
106 Cheryl Brown 0 14 0.6364 0.5940
106 Steward Hogans 0 11 0.6364 0.4667
108 Paul Presti 0 10 0.6358 0.4239
109 Amy Asberry 0 11 0.6347 0.4654
110 Jonathan Smith 0 13 0.6340 0.5495
111 Kristen White 0 15 0.6339 0.6339
111 Rachel Follo 1 15 0.6339 0.6339
113 Tara Bridgett 0 8 0.6320 0.3371
114 Keisha Vasquez 1 11 0.6310 0.4627
115 Diance Durand 1 14 0.6303 0.5883
116 Jason Jackson 0 14 0.6298 0.5878
117 Matthew Olguin 0 15 0.6295 0.6295
118 Yiming Hu 0 12 0.6278 0.5022
119 Jordan Forwood 0 6 0.6277 0.2511
120 Kenneth Nielsen 0 8 0.6260 0.3339
121 Vincent Scannelli 0 14 0.6209 0.5795
122 Melissa Printup 0 15 0.6205 0.6205
123 Anthony Rockemore 0 12 0.6201 0.4961
124 Desmond Jenkins 0 7 0.6168 0.2878
125 Bunnaro Sun 0 13 0.6162 0.5340
126 Louie Renew 1 14 0.6154 0.5744
126 Marcus Evans 0 14 0.6154 0.5744
126 Terry Hardison 0 13 0.6154 0.5333
129 Wayne Schofield 1 14 0.6106 0.5699
130 David Hadley 0 5 0.6104 0.2035
131 Gary Lawrence 1 15 0.6071 0.6071
132 Ronald Schmidt 0 13 0.6062 0.5254
133 Jose Torres Mendoza 0 11 0.6037 0.4427
134 Wayne Gokey 0 5 0.6026 0.2009
135 Jonathan Knight 0 6 0.6022 0.2409
136 Megan Fitzgerald 0 10 0.6013 0.4009
137 Jack Wheeler 0 14 0.6000 0.5600
137 Ryan Shipley 0 11 0.6000 0.4400
139 Kevin Green 0 6 0.5978 0.2391
140 Derrick Elam 0 10 0.5973 0.3982
141 Kyle May 0 14 0.5952 0.5555
142 Adam Konkle 0 2 0.5938 0.0792
142 Cherylynn Vidal 0 4 0.5938 0.1583
142 Jeffrey Dusza 0 2 0.5938 0.0792
145 Robert Lynch 0 13 0.5918 0.5129
146 Thomas Cho 0 7 0.5888 0.2748
147 Jason Miranda 0 6 0.5824 0.2330
148 Jennifer Wilson 0 6 0.5806 0.2322
149 Sheryl Claiborne-Smith 0 11 0.5741 0.4210
150 Steven Webster 0 12 0.5730 0.4584
151 Richard Conkle 0 12 0.5691 0.4553
152 Robert Martin 0 13 0.5625 0.4875
153 Joseph Martin 0 7 0.5607 0.2617
154 Min Choi 0 5 0.5526 0.1842
155 Lawrence Thuotte 1 4 0.5484 0.1462
156 Donald Park 0 3 0.5435 0.1087
157 Gabriel Quinones 0 9 0.5407 0.3244
158 Akilah Gamble 1 10 0.5333 0.3555
159 David Kim 0 2 0.5312 0.0708
159 Monte Henderson 0 2 0.5312 0.0708
161 Jamie Ainsleigh-Wong 0 6 0.5217 0.2087
162 Andrew Gray 0 13 0.5153 0.4466
163 Jay Kelly 0 7 0.5140 0.2399
164 Antonio Chapa 0 1 0.5000 0.0333
164 Vincent Kandian 0 4 0.5000 0.1333
164 Zachary Brosemer 0 1 0.5000 0.0333
167 Ashley Johnson 0 6 0.4831 0.1932
168 Ashlyn Dortch 0 5 0.4805 0.1602
169 Gabrieal Feiling 0 2 0.4688 0.0625
170 Jasprin Smith 0 1 0.3750 0.0250
170 Robert Epps 0 1 0.3750 0.0250

Adjusted Season Leaderboard

Season Leaderboard (Adjusted Season Percent)
Week 15
Season Rank Name Donuts Won Weeks Picked Season Percent Adj Season Percent Season Trend
1 Aubrey Conn 2 15 0.7188 0.7188
2 Michael Pacifico 1 15 0.7143 0.7143
2 Robert Gelo 1 15 0.7143 0.7143
4 Marc Agne 2 15 0.7054 0.7054
5 Chester Todd 1 15 0.7009 0.7009
6 Gregory Brown 2 15 0.6964 0.6964
6 Randy Dick 1 15 0.6964 0.6964
8 Chris Papageorge 0 15 0.6920 0.6920
8 Christopher Sims 2 15 0.6920 0.6920
8 Heather Ellenberger 0 15 0.6920 0.6920
8 Jeremy Stieler 1 15 0.6920 0.6920
12 Anthony Bloss 0 15 0.6875 0.6875
12 Brayant Rivera 0 15 0.6875 0.6875
12 Keven Talbert 2 15 0.6875 0.6875
15 George Sweet 2 15 0.6830 0.6830
15 Matthew Schultz 0 15 0.6830 0.6830
15 Shaun Dahl 1 15 0.6830 0.6830
15 Shawn Carden 0 15 0.6830 0.6830
19 Robert Cunningham 0 14 0.7308 0.6821
20 Daniel Baller 0 15 0.6786 0.6786
20 Kevin Buettner 1 15 0.6786 0.6786
20 Kevin Kehoe 0 15 0.6786 0.6786
23 Edward Ford 0 15 0.6741 0.6741
23 Nicole Dike 0 15 0.6741 0.6741
23 Pablo Burgosramos 1 15 0.6741 0.6741
26 Jason Schattel 0 15 0.6696 0.6696
26 Ryan Cvik 0 15 0.6696 0.6696
26 Scott Lefton 0 15 0.6696 0.6696
26 Stephen Bush 2 15 0.6696 0.6696
30 Nathan Brown 1 14 0.7143 0.6667
30 Randolph Tidd 1 14 0.7143 0.6667
32 Richard Beeghley 1 15 0.6652 0.6652
33 Bradley Hobson 1 14 0.7095 0.6622
34 Bruce Williams 0 14 0.7081 0.6609
35 George Brown 1 15 0.6607 0.6607
36 Jennifer Arty 0 15 0.6562 0.6562
36 Karen Richardson 1 15 0.6562 0.6562
38 Patrick Tynan 1 14 0.6986 0.6520
39 Antonio Mitchell 0 15 0.6518 0.6518
39 Cade Martinez 0 15 0.6518 0.6518
39 Jeffrey Rudderforth 0 15 0.6518 0.6518
39 Michael Branson 0 15 0.6518 0.6518
39 Michael Moss 0 15 0.6518 0.6518
39 Montee Brown 1 15 0.6518 0.6518
39 Walter Archambo 0 15 0.6518 0.6518
46 William Schouviller 1 14 0.6938 0.6475
47 Jonathon Leslein 0 15 0.6473 0.6473
47 Thomas Mccoy 0 15 0.6473 0.6473
49 Thomas Brenstuhl 1 14 0.6923 0.6461
50 Ramar Williams 0 15 0.6429 0.6429
50 Trevor Macgavin 1 15 0.6429 0.6429
52 David Dupree 0 14 0.6857 0.6400
53 David Humes 0 15 0.6384 0.6384
54 Karen Coleman 0 14 0.6827 0.6372
55 Kristen White 0 15 0.6339 0.6339
55 Rachel Follo 1 15 0.6339 0.6339
57 Zechariah Ziebarth 1 14 0.6762 0.6311
58 Matthew Olguin 0 15 0.6295 0.6295
59 Jennifer Bouland 0 14 0.6731 0.6282
60 Daniel Halse 0 14 0.6714 0.6266
61 Darryle Sellers 0 14 0.6683 0.6237
61 Rafael Torres 0 14 0.6683 0.6237
63 Melissa Printup 0 15 0.6205 0.6205
64 Daniel Major 0 14 0.6603 0.6163
65 Earl Dixon 0 14 0.6571 0.6133
66 Kamar Morgan 1 14 0.6538 0.6102
66 Nahir Shepard 0 14 0.6538 0.6102
68 Gary Lawrence 1 15 0.6071 0.6071
69 Jared Kaanga 0 14 0.6442 0.6013
69 Nicholas Nguyen 1 14 0.6442 0.6013
71 Nicholas Cinco 0 13 0.6927 0.6003
72 Christopher Mulcahy 0 14 0.6429 0.6000
72 Erik Neumann 0 13 0.6923 0.6000
72 Jeffrey Zornes 0 14 0.6429 0.6000
75 Joshua Tracey 1 14 0.6381 0.5956
76 Cheryl Brown 0 14 0.6364 0.5940
77 Diance Durand 1 14 0.6303 0.5883
78 Jason Jackson 0 14 0.6298 0.5878
79 Bryson Scott 0 13 0.6753 0.5853
80 Vincent Scannelli 0 14 0.6209 0.5795
81 Louie Renew 1 14 0.6154 0.5744
81 Marcus Evans 0 14 0.6154 0.5744
83 Michael Moore 1 13 0.6615 0.5733
84 Wayne Schofield 1 14 0.6106 0.5699
85 Brian Patterson 0 13 0.6513 0.5645
86 Darvin Graham 0 13 0.6510 0.5642
86 Michelle Fraterrigo 0 13 0.6510 0.5642
88 Jack Wheeler 0 14 0.6000 0.5600
89 Brian Hollmann 1 12 0.6989 0.5591
90 James Small 0 13 0.6443 0.5584
91 Kyle May 0 14 0.5952 0.5555
92 Jonathan Smith 0 13 0.6340 0.5495
93 Pamela Augustine 0 12 0.6798 0.5438
94 Robert Sokol 0 12 0.6705 0.5364
95 Bunnaro Sun 0 13 0.6162 0.5340
96 Terry Hardison 0 13 0.6154 0.5333
97 Clevante Granville 0 12 0.6648 0.5318
98 George Mancini 0 12 0.6591 0.5273
99 Ronald Schmidt 0 13 0.6062 0.5254
100 Paul Seitz 0 12 0.6554 0.5243
101 Ryan Baum 0 12 0.6500 0.5200
102 Noah Gosswiller 0 12 0.6461 0.5169
103 Robert Lynch 0 13 0.5918 0.5129
104 Matthew Blair 0 10 0.7671 0.5114
105 Brandon Parks 0 12 0.6384 0.5107
106 Yiming Hu 0 12 0.6278 0.5022
107 Anthony Rockemore 0 12 0.6201 0.4961
108 Philip Driskill 1 11 0.6747 0.4948
109 Robert Martin 0 13 0.5625 0.4875
110 Travis Delagardelle 1 11 0.6627 0.4860
111 David Plate 0 11 0.6566 0.4815
112 Anthony Brinson 0 11 0.6524 0.4784
113 Michael Linder 0 10 0.7143 0.4762
114 Steward Hogans 0 11 0.6364 0.4667
115 Amy Asberry 0 11 0.6347 0.4654
116 Heather Kohler 0 10 0.6980 0.4653
117 Keisha Vasquez 1 11 0.6310 0.4627
118 Steven Webster 0 12 0.5730 0.4584
119 Richard Conkle 0 12 0.5691 0.4553
120 Andrew Gray 0 13 0.5153 0.4466
121 Jose Torres Mendoza 0 11 0.6037 0.4427
122 Ryan Shipley 0 11 0.6000 0.4400
123 Steven Maisonneuve 1 9 0.7231 0.4339
124 Brittany Pillar 1 8 0.8087 0.4313
125 Paul Presti 0 10 0.6358 0.4239
126 Sheryl Claiborne-Smith 0 11 0.5741 0.4210
127 Megan Fitzgerald 0 10 0.6013 0.4009
128 Derrick Elam 0 10 0.5973 0.3982
129 Jeremy Mounce 0 8 0.7000 0.3733
130 Akilah Gamble 1 10 0.5333 0.3555
131 Tara Bridgett 0 8 0.6320 0.3371
132 Kenneth Nielsen 0 8 0.6260 0.3339
133 Gabriel Quinones 0 9 0.5407 0.3244
134 Desmond Jenkins 0 7 0.6168 0.2878
135 Thomas Cho 0 7 0.5888 0.2748
136 Joseph Martin 0 7 0.5607 0.2617
137 Jordan Forwood 0 6 0.6277 0.2511
138 Jonathan Knight 0 6 0.6022 0.2409
139 Jay Kelly 0 7 0.5140 0.2399
140 Kevin Green 0 6 0.5978 0.2391
141 Jason Miranda 0 6 0.5824 0.2330
142 Jennifer Wilson 0 6 0.5806 0.2322
143 Jamie Ainsleigh-Wong 0 6 0.5217 0.2087
144 David Hadley 0 5 0.6104 0.2035
145 Wayne Gokey 0 5 0.6026 0.2009
146 Ashley Johnson 0 6 0.4831 0.1932
147 Min Choi 0 5 0.5526 0.1842
148 Ashlyn Dortch 0 5 0.4805 0.1602
149 Cherylynn Vidal 0 4 0.5938 0.1583
150 Lawrence Thuotte 1 4 0.5484 0.1462
151 Jeremy Krammes 0 3 0.6957 0.1391
152 Vincent Kandian 0 4 0.5000 0.1333
153 George Hall 0 3 0.6522 0.1304
154 Donald Park 0 3 0.5435 0.1087
155 Terrence Lee 0 2 0.6897 0.0920
156 Adam Konkle 0 2 0.5938 0.0792
156 Jeffrey Dusza 0 2 0.5938 0.0792
158 David Kim 0 2 0.5312 0.0708
158 Monte Henderson 0 2 0.5312 0.0708
160 Gabrieal Feiling 0 2 0.4688 0.0625
161 Clayton Grimes 0 1 0.8750 0.0583
162 Tanaysa Henderson 0 1 0.8571 0.0571
163 Brian Holder 0 1 0.7500 0.0500
163 Sandra Carter 0 1 0.7500 0.0500
163 Wallace Savage 0 1 0.7500 0.0500
166 Ryan Wiggins 0 1 0.7333 0.0489
167 Daniel Gray 0 1 0.6875 0.0458
168 Antonio Chapa 0 1 0.5000 0.0333
168 Zachary Brosemer 0 1 0.5000 0.0333
170 Jasprin Smith 0 1 0.3750 0.0250
170 Robert Epps 0 1 0.3750 0.0250

Data

---
title: "2024 NFL Moneyline Picks"
output: 
  flexdashboard::flex_dashboard:
    theme:
      version: 4
      bootswatch: spacelab
    orientation: rows
    vertical_layout: fill
    social: ["menu"]
    source_code: embed
    navbar:
      - { title: "Created by: Daniel Baller", icon: "fa-github", href: "https://github.com/danielpballer"  }
---


```{r setup, include=FALSE}
#    source_code: embed
library(flexdashboard)
library(tidyverse)
library(data.table)
library(formattable)
library(ggpubr)
library(ggrepel)
library(gt)
library(glue)
library(ggthemes)
library(hrbrthemes)
library(sparkline)
library(plotly)
library(htmlwidgets)
library(mdthemes)
library(ggtext)
library(ggnewscale)
library(DT)
source("./Functions/functions2.R")

thematic::thematic_rmd(font = "auto")

```

```{r Reading in our picks files, include=FALSE}
current_week = 15 #Set what week it is
week_1 = read_csv("./CSV_Data_Files/2024 NFL Week 1.csv") %>% 
  mutate(Name = str_to_title(Name))
week_2 = read_csv("./CSV_Data_Files/2024 NFL Week 2.csv")%>% 
  mutate(Name = str_to_title(Name))
week_3 = read_csv("./CSV_Data_Files/2024 NFL Week 3.csv")%>% 
  mutate(Name = str_to_title(Name))
week_4 = read_csv("./CSV_Data_Files/2024 NFL Week 4.csv")%>%
 mutate(Name = str_to_title(Name))
week_5 = read_csv("./CSV_Data_Files/2024 NFL Week 5.csv")%>% 
  mutate(Name = str_to_title(Name))
week_6 = read_csv("./CSV_Data_Files/2024 NFL Week 6.csv")%>% 
  mutate(Name = str_to_title(Name))
week_7 = read_csv("./CSV_Data_Files/2024 NFL Week 7.csv")%>% 
  mutate(Name = str_to_title(Name))
week_8 = read_csv("./CSV_Data_Files/2024 NFL Week 8.csv")%>% 
  mutate(Name = str_to_title(Name))
 week_9 = read_csv("./CSV_Data_Files/2024 NFL Week 9.csv")%>% 
  mutate(Name = str_to_title(Name))
week_10 = read_csv("./CSV_Data_Files/2024 NFL Week 10.csv")%>% 
  mutate(Name = str_to_title(Name))
week_11 = read_csv("./CSV_Data_Files/2024 NFL Week 11.csv")%>% 
  mutate(Name = str_to_title(Name))
week_12 = read_csv("./CSV_Data_Files/2024 NFL Week 12.csv")%>% 
  mutate(Name = str_to_title(Name))
week_13 = read_csv("./CSV_Data_Files/2024 NFL Week 13.csv")%>% 
  mutate(Name = str_to_title(Name))
week_14 = read_csv("./CSV_Data_Files/2024 NFL Week 14.csv")%>% 
  mutate(Name = str_to_title(Name))
week_15 = read_csv("./CSV_Data_Files/2024 NFL Week 15.csv")%>% 
  mutate(Name = str_to_title(Name))
# week_16 = read_csv("./CSV_Data_Files/2024 NFL Week 16.csv")%>% 
#  mutate(Name = str_to_title(Name))
# week_17 = read_csv("./CSV_Data_Files/2024 NFL Week 17.csv")%>% 
#  mutate(Name = str_to_title(Name))
# week_18 = read_csv("./CSV_Data_Files/2024 NFL Week 18.csv")%>% 
#  mutate(Name = str_to_title(Name))
# week_19 = read_csv("./CSV_Data_Files/2024 NFL Wild Card.csv")%>% 
#  mutate(Name = str_to_title(Name))
# week_20 = read_csv("./CSV_Data_Files/2024 NFL Divisional Round.csv")%>% 
#  mutate(Name = str_to_title(Name))
# week_21 = read_csv("./CSV_Data_Files/2024 NFL Conference Round.csv")%>% 
#  mutate(Name = str_to_title(Name))
# week_22 = read_csv("./CSV_Data_Files/2024 NFL Super Bowl.csv")%>% 
#  mutate(Name = str_to_title(Name))

#reading in scores
Scores = read_csv(glue::glue("./CSV_Data_Files/NFL_Scores_{current_week}.csv")) 

#reading in CBS Prediction Records
cbs = read_csv(glue::glue("./CSV_Data_Files/CBS_Experts_{current_week}.csv")) %>% 
  mutate(Percent = round(Percent,4))
cbs_season = read_csv(glue::glue("./CSV_Data_Files/CBS_Experts_Season_{current_week}.csv"))

#reading in ESPN Prediction Records
espn = read_csv(glue::glue("./CSV_Data_Files/ESPN_Experts_{current_week}.csv"))%>% 
  mutate(Percent = round(Percent,4))
espn_season = read_csv(glue::glue("./CSV_Data_Files/ESPN_Experts_Season_{current_week}.csv"))%>% 
  mutate(Percent = round(Percent,4))

#Odds not working for the 2024 season.  Need to fix scrape code for next year.
#Reading in the moneyline odds for each team and cleaning the team names
# odds_wk1 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_1.csv"))
# odds_wk2 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_2.csv"))
# odds_wk3 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_3.csv"))
# odds_wk4 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_4.csv"))
# odds_wk5 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_5.csv"))
# odds_wk6 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_6.csv"))
# odds_wk7 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_7.csv"))
# odds_wk8 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_8.csv"))
# odds_wk9 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_9.csv"))
# odds_wk10 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_10.csv"))
# odds_wk11 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_11.csv"))
# odds_wk12 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_12.csv"))
# odds_wk13 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_13.csv"))
# odds_wk14 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_14.csv"))
# odds_wk15 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_15.csv"))
# odds_wk16 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_16.csv"))
# odds_wk17 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_17.csv"))
# odds_wk18 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_18.csv"))
# odds_wk19 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_19.csv"))
# odds_wk20 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_20.csv"))
# odds_wk21 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_21.csv"))
# odds_wk22 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_22.csv"))

####################UPDATE THESE###############################
inst.picks = list(week_1, week_2, week_3, week_4, week_5, week_6, week_7, week_8 , week_9, week_10, week_11, week_12, week_13, week_14, week_15) #, week_16, week_17 , week_18, week_19 , week_20, week_21, week_22) #add in the additional weeks
# odds = rbind(odds_wk1, odds_wk2, odds_wk3, odds_wk4, odds_wk5, odds_wk6, odds_wk7, odds_wk8,
#              odds_wk9, odds_wk10, odds_wk11, odds_wk12) #add in the additional weeks
####################END OF UPDATE##############################

weeks = as.list(seq(1:current_week)) #creating a list of each week number
```

```{r read in scores clean data, include=FALSE}
#Cleaning Odds Data
# cl_odds = odds_cleaning(odds)

#Cleaning scores data
Scores = cleaning2(Scores)

#creating a list of winners for each week
winners = map(weeks, weekly_winners)

#creating a vector of this weeks winners
this_week = pull(winners[[length(winners)]])  

#Getting the number of games for each week
weekly_number_of_games = map_dbl(weeks, week_number_games)
```

```{r Group Predictions, include=FALSE}
#Creating the list of everyones predictions each week.
games = map(inst.picks, games_fn)

#Creating the prediction table.  
pred_table = map(games, pred_table_fn)

#Adding who won to the predictions
with_winners = map2(pred_table, winners, adding_winners)

#Creating results for each week.
results = map2(with_winners,weekly_number_of_games, results_fn)
```


```{r Displaying Group Results, echo=FALSE}
#Displaying the group results

inst_group_table = results[[length(results)]] %>% gt() %>% 
  cols_align(
    align = "center") %>% 
   tab_header(
    title = md("This Week's Predictions"),
    #subtitle = md(glue("Week {length(results)}"))
    ) %>% 
   tab_style(
    style = cell_text(color = "red", weight = "bold"),
    locations = cells_body(
      columns = c(Correct),
      rows = Correct =="No"
    )) %>% 
   tab_style(
    style = cell_text(color = "green", weight = "bold"),
    locations = cells_body(
      columns = c(Correct),
      rows = Correct =="Yes"
    )) %>% 
  tab_options(
    data_row.padding = px(3),
    container.height = "100%"
   )
```

```{r Weekly and season Group Results, include=FALSE}
# Printing the weekly and season win percentage     

#how many games correct, incorrect, and not picked each week
weekly_group_correct = map(results, weekly_group_correct_fn)  

#how many games were picked each week
weekly_games_picked = map2(weekly_group_correct, weekly_number_of_games, weekly_games_picked_fn)

#Calculating the number of correct picks for each week
weekly_group_correct_picks = map(weekly_group_correct, weekly_group_correct_picks_fn)

# Code to manually hard code in week where we get 0 games correct
# ##### Remove this line before next season 
# weekly_group_correct_picks[[21]]=0

#Calculating weekly win percentage
weekly_win_percentage = map2(weekly_group_correct_picks, weekly_games_picked, weekly_win_percentage_fn)

#Calculating season win percentage
season_win_percentage = round(sum(unlist(weekly_group_correct_picks))/sum(unlist(weekly_games_picked)),4)

#Calculating number of games picked this season
season_games = sum(unlist(weekly_games_picked))

#calculating season wins
season_wins = sum(unlist(weekly_group_correct_picks))

#calculating the number of people who picked this week
Total = dim(inst.picks[[length(weeks)]])[1]
```

```{r plotting group results, include=FALSE}
#Previous Weeks
group_season_for_plotting = unlist(weekly_win_percentage) %>% as.data.frame() %>% 
  rename(`Win Percentage` = ".") %>% 
  add_column(Week = unlist(weeks))
```

```{r Plotting the group results, echo=FALSE}
inst_group_season_plot = group_season_for_plotting %>% 
ggplot(aes(x = as.factor(Week), y = `Win Percentage`))+
  geom_point()+
  geom_path(aes(x = Week))+
  ylim(c(0, 1)) +
  xlab("NFL Week") + 
  ylab("Correct Percentage")+
  ggtitle("Weekly Group Correct Percentage")+
  theme_classic()+
  theme(plot.title = element_text(hjust = 0.5, size = 18))
```

```{r beating cbs week, include=FALSE}
#Creating a list of correct percentages for each week.
cbs_weekly_percent = map(weeks, cbs_percent)

#Creating a list of how many cbs experts we beat each week.
cbs_experts_beat = map2(cbs_weekly_percent, weekly_win_percentage, experts_beat)

#Creating a list of how many cbs experts picked each week.  
cbs_experts_total = map(cbs_weekly_percent, experts_tot)
```

```{r beating cbs season, include=FALSE}
#Creating a list of correct percentages for each week.
cbs_season_percent = map(weeks, cbs_season_percent)

#Creating a list of how many cbs experts we beat each week.
cbs_experts_beat_season = map2(cbs_season_percent, season_win_percentage, experts_beat)

#Creating a list of how many cbs experts picked each week.  
cbs_experts_season_total = map(cbs_season_percent, experts_tot)
```

```{r beating ESPN week, include=FALSE}
#Creating a list of correct percentages for each week.
espn_weekly_percent = map(weeks, espn_percent)

#Creating a list of how many cbs experts we beat each week.
espn_experts_beat = map2(espn_weekly_percent, weekly_win_percentage, experts_beat)

#Creating a list of how many cbs experts picked each week.  
espn_experts_total = map(espn_weekly_percent, experts_tot)
```

```{r beating ESPN season, include=FALSE}
#Creating a list of correct percentages for each week.
espn_season_percent = map(weeks, espn_season_percent)

#Creating a list of how many cbs experts we beat each week.
espn_experts_beat_season = map2(espn_season_percent, season_win_percentage, experts_beat)

#Creating a list of how many cbs experts picked each week.  
espn_experts_season_total = map(espn_season_percent, experts_tot)
```

```{r individual results, include=FALSE}
#Creating a list of individual results for each week.
weekly_indiv = pmap(list(inst.picks, winners, weeks), indiv_weekly_pred)

#Combining each week into one dataframe and calculating percentage Correct for this week.  
full_season = weekly_indiv %>% reduce(full_join, by = "Name") %>% 
  mutate(Percent = round(pull(.[,ncol(.)]/weekly_number_of_games[[length(weekly_number_of_games)]]),4)) 

#Creating a dataframe with only the weekly picks
a = full_season %>% select(starts_with("Week"))

#Creating a vector of how many weeks each person picked over the season
tot_week = NULL
help = NULL
for (i in 1:dim(a)[1]){
  for(j in 1:length(a)){
    help[j] = ifelse(is.na(a[i,j])==T,0,1)
    tot_week[i] = sum(help)
  }
}

#Creating a vector of how many games each person picked over the season
tot_picks= NULL
help = NULL
for (i in 1:dim(a)[1]){
  for(j in 1:length(a)){
    help[j] = unlist(weekly_games_picked)[j]*ifelse(is.na(a[i,j])==T,0,1)
    tot_picks[i] = sum(help)
  }
}

#Creatign a vector of how many games each person picked correct over the season
tot_correct = NULL
help = NULL
for (i in 1:dim(a)[1]){
  tot_correct[i] = sum(a[i,], na.rm = T)
}

#adding how many weeks each person picked, season correct percentage, and adjusted season percentag to the data frame and sorting the data
indiv_disp = full_season %>% add_column(`Weeks Picked` = tot_week) %>%
  add_column(tot_correct)%>%
  add_column(tot_picks)%>%
  mutate(`Season Percent` = round(tot_correct/tot_picks,4))%>%
  mutate(`Adj Season Percent` = round(`Season Percent`*(tot_week/length(a)),4)) %>%
  select(-tot_correct, -tot_picks) %>%
  arrange(desc(Percent), desc(`Season Percent`)) %>%
  mutate(Percent = ifelse(is.na(Percent)==T, 0, Percent))
```


```{r individual percentages, include=FALSE}
#Calculating individual percentages for each week.
weekly_indiv_percent = map2(weekly_indiv, as.list(weekly_number_of_games), indiv_percent) %>% reduce(full_join, by = "Name")

weekly_indiv_percent_plot = weekly_indiv_percent %>% 
  pivot_longer(cols = starts_with("Week"), names_to = "Week", values_to = "Percent")%>%
  mutate(Percent = ifelse(is.na(Percent)==T, 0, Percent)) %>% 
  mutate(Week = as.factor(Week))

levels = NULL
for(i in 1:length(weeks)){
  levels[i] = glue("Week {i}")  
}

weekly_indiv_percent_plot = weekly_indiv_percent_plot %>%
  mutate(Week = factor(Week, levels))
```

```{r sparklines, include=FALSE}
#adding sparklines
plot_group = function(name, df){
  plot_object = 
    ggplot(data = df,
           aes(x = as.factor(Week), y=Percent, group = 1))+
    geom_path(size = 7)+
    scale_y_continuous(limits = c(0,1))+
    theme_void()+
    theme(legend.position = "none")
  return(plot_object)
}

sparklines = 
  weekly_indiv_percent_plot %>% 
  group_by(Name) %>% 
  nest() %>% 
  mutate(plot = map2(Name, data, plot_group)) %>% 
  select(-data)
  
indiv_disp_2 = indiv_disp %>% 
  inner_join(sparklines, by = "Name") %>% 
  mutate(`Season Trend` = NA)
```

```{r Printing Individual Table2, echo=FALSE}
# Printing the individual Table
indiv_table = indiv_disp_2 %>% gt() %>% 
  cols_align(
    align = "center") %>% 
   tab_header(
    title = md("Individual Results"),
    subtitle = md(glue("Week {length(weeks)}"))
    ) %>% 
   tab_style(
    style = cell_text(color = "red", weight = "bold"),
    locations = cells_body(
      columns = c(Percent),
      rows = Percent<.5
    )) %>% 
   tab_style(
    style = cell_text(color = "green", weight = "bold"),
    locations = cells_body(
      columns = c(Percent),
      rows = Percent>.5
    )) %>% 
     tab_style(
    style = cell_text(color = "red", weight = "bold"),
    locations = cells_body(
      columns = c(`Season Percent`),
      rows = `Season Percent`<.5
    )) %>% 
   tab_style(
    style = cell_text(color = "green", weight = "bold"),
    locations = cells_body(
      columns = c(`Season Percent`),
      rows = `Season Percent`>.5
    ))%>% 
     tab_style(
    style = cell_text(color = "red", weight = "bold"),
    locations = cells_body(
      columns = c(`Adj Season Percent`),
      rows = `Adj Season Percent`<.5
    )) %>% 
   tab_style(
    style = cell_text(color = "green", weight = "bold"),
    locations = cells_body(
      columns = c(`Adj Season Percent`),
      rows = `Adj Season Percent`>.5
    )) %>% 
  tab_options(
    container.width = pct(100),
    data_row.padding = px(1),
    container.height = "100%"
   ) %>%
    tab_spanner(
    label = "Weekly # Correct",
    columns = starts_with(c("Week "))
  ) %>% 
  text_transform(
    locations = cells_body(c(`Season Trend`)),
    fn = function(x){
      map(indiv_disp_2$plot, ggplot_image, height = px(30), aspect_ratio = 4)
                 }) %>%
  cols_hide(c(plot))

indiv_winners = indiv_disp_2 %>% filter(Percent == max(Percent)) %>% select(Name) %>% pull() %>% paste(collapse = ", ")
indiv_season = indiv_disp_2 %>% filter(`Season Percent` == max(`Season Percent`)) %>% select(Name) %>% pull() %>% paste(collapse = ", ")
indiv_season_adj = indiv_disp_2 %>% filter(`Adj Season Percent` == max(`Adj Season Percent`)) %>% select(Name) %>% pull()%>% paste(collapse = ", ")
```

```{r Printing Season Leaderboard, echo=FALSE}
# Printing the Season Leaderboard
  
season_leaderboard_disp = indiv_disp_2 %>% select(Name, starts_with("Week ")) %>% 
  pivot_longer(starts_with("Week"),names_to = "Week", values_to = "Correct") %>% 
  group_by(Week) %>% 
  mutate(Correct = case_when(is.na(Correct)==T~0, 
                             TRUE~Correct)) %>% 
  mutate(Donut = case_when(Correct==max(Correct)~1,
                           TRUE~0))  %>% 
  ungroup() %>% 
  group_by(Name) %>% 
  summarise(`Donuts Won` = sum(Donut)) %>% 
  #mutate(`Donuts Won` = strrep("award,", Donuts)) %>% 
  right_join(.,indiv_disp_2) %>% 
  select(-starts_with("Week "), -Percent) %>% 
  mutate(`Season Rank` = min_rank(desc(`Season Percent`)),.before = Name) %>% 
  arrange(`Season Rank`) 
  
season_leaderboard = season_leaderboard_disp %>% 
  gt() %>% 
  cols_align(
    align = "center") %>% 
   tab_header(
    title = md("Season Leaderboard (Season Percent)"),
    subtitle = md(glue("Week {length(weeks)}"))
    ) %>% 
  # fmt_icon(
  #   columns = `Donuts Won`,
  #   fill_color = "gold",
  # ) %>%
  tab_style(
    style = cell_text(color = "red", weight = "bold"),
    locations = cells_body(
      columns = c(`Season Percent`),
      rows = `Season Percent`<.5
    )) %>% 
   tab_style(
    style = cell_text(color = "green", weight = "bold"),
    locations = cells_body(
      columns = c(`Season Percent`),
      rows = `Season Percent`>.5
    ))%>% 
     tab_style(
    style = cell_text(color = "red", weight = "bold"),
    locations = cells_body(
      columns = c(`Adj Season Percent`),
      rows = `Adj Season Percent`<.5
    )) %>% 
   tab_style(
    style = cell_text(color = "green", weight = "bold"),
    locations = cells_body(
      columns = c(`Adj Season Percent`),
      rows = `Adj Season Percent`>.5
    )) %>% 
  tab_options(
    container.width = pct(100),
    data_row.padding = px(1),
    container.height = "100%"
   ) %>%
    tab_spanner(
    label = "Weekly # Correct",
    columns = starts_with(c("Week "))
  ) %>% 
  text_transform(
    locations = cells_body(c(`Season Trend`)),
    fn = function(x){
      map(season_leaderboard_disp$plot, ggplot_image, height = px(30), aspect_ratio = 4)
                 }) %>%
  cols_hide(columns = c(plot))
```

```{r Printing Adj Season Leaderboard, echo=FALSE}
# Printing the Adj Season Leaderboard
  
adj_season_leaderboard_disp = indiv_disp_2 %>% select(Name, starts_with("Week ")) %>% 
  pivot_longer(starts_with("Week"),names_to = "Week", values_to = "Correct") %>% 
  group_by(Week) %>% 
  mutate(Correct = case_when(is.na(Correct)==T~0, 
                             TRUE~Correct)) %>% 
  mutate(Donut = case_when(Correct==max(Correct)~1,
                           TRUE~0))  %>% 
  ungroup() %>% 
  group_by(Name) %>% 
  summarise(`Donuts Won` = sum(Donut)) %>% 
  #mutate(`Donuts Won` = strrep("award,", Donuts)) %>% 
  right_join(.,indiv_disp_2) %>% 
  select(-starts_with("Week "), -Percent) %>% 
  mutate(`Season Rank` = min_rank(desc(`Adj Season Percent`)),.before = Name) %>% 
  arrange(`Season Rank`)

adj_season_leaderboard = adj_season_leaderboard_disp %>% 
  gt() %>% 
  cols_align(
    align = "center") %>% 
   tab_header(
    title = md("Season Leaderboard (Adjusted Season Percent)"),
    subtitle = md(glue("Week {length(weeks)}"))
    ) %>% 
  # fmt_icon(
  #   columns = `Donuts Won`,
  #   fill_color = "gold",
  # ) %>%
  tab_style(
    style = cell_text(color = "red", weight = "bold"),
    locations = cells_body(
      columns = c(`Season Percent`),
      rows = `Season Percent`<.5
    )) %>% 
   tab_style(
    style = cell_text(color = "green", weight = "bold"),
    locations = cells_body(
      columns = c(`Season Percent`),
      rows = `Season Percent`>.5
    ))%>% 
     tab_style(
    style = cell_text(color = "red", weight = "bold"),
    locations = cells_body(
      columns = c(`Adj Season Percent`),
      rows = `Adj Season Percent`<.5
    )) %>% 
   tab_style(
    style = cell_text(color = "green", weight = "bold"),
    locations = cells_body(
      columns = c(`Adj Season Percent`),
      rows = `Adj Season Percent`>.5
    )) %>% 
  tab_options(
    container.width = pct(100),
    data_row.padding = px(1),
    container.height = "100%"
   ) %>%
    tab_spanner(
    label = "Weekly # Correct",
    columns = starts_with(c("Week "))
  ) %>% 
  text_transform(
    locations = cells_body(c(`Season Trend`)),
    fn = function(x){
      map(adj_season_leaderboard_disp$plot, ggplot_image, height = px(30), aspect_ratio = 4)
                 }) %>%
  cols_hide(columns = c(plot))
```


```{r instructor formattable, echo=FALSE}
improvement_formatter <- 
  formatter("span", 
            style = x ~ formattable::style(
              font.weight = "bold", 
              color = ifelse(x > .5, "green", ifelse(x < .5, "red", "black"))),
             x ~ icontext(ifelse(x == max(x), "star", ""), x))

indiv_disp_3 = indiv_disp_2 %>% select(-plot)
indiv_disp_3$`Season Trend` = apply(indiv_disp_3[,2:(1+length(weeks))], 1, FUN = function(x) as.character(htmltools::as.tags(sparkline(as.numeric(x), type = "line", chartRangeMin = 0, chartRangeMax = 1, fillColor = "white"))))

indiv_table_2 = as.htmlwidget(formattable(indiv_disp_3, 
                                align = c("l", rep("c", NROW(indiv_disp_3)-1)),
              list(`Season Percent` = color_bar("#FA614B"),
              `Season Percent`= improvement_formatter,
              `Adj Season Percent`= improvement_formatter)))
              
indiv_table_2$dependencies = c(indiv_table_2$dependencies, htmlwidgets:::widget_dependencies("sparkline", "sparkline"))
```

```{r Plotting individual results over the season2, eval=FALSE, include=FALSE, out.width="100%"}
#Creating the individual plot.  
inst_indiv_plots = weekly_indiv_percent_plot %>% 
  ggplot(aes(x = factor(Week), y = Percent, color = Name))+
  geom_point()+
  geom_path(aes(x = as.factor(Week), y = Percent, color = Name, 
                group = Name))+
  ylim(c(0, 1)) +
  labs(x = "NFL Week", 
       y = "Correct Percentage", 
       title = "Weekly Individual Correct Percentage")+
  facet_wrap(~Name)+
  theme_classic()+
  theme(legend.position = "none",
        plot.title = element_text(hjust = 0.5, size = 18),
        axis.text.x=element_text(angle =45, vjust = 1, hjust = 1))
```

```{r data for data page}
inst.data = map2(inst.picks, weeks, disp_data) %>% bind_rows()
```


```{r fivethirtyeight}
inst_538 = map(results, five38) %>% unlist() %>% sum()
```

```{r pregame, eval=FALSE, include=FALSE}
#Predictions for the week

#Creating the list of group predictions each week.
games = map(inst.picks, games_fn)

#Creating the prediction table.  
pred_table = map(games, pred_table_fn)

#Printing table of instructor predictions
pred_table[[length(pred_table)]] %>% mutate(Game = row_number()) %>% 
  rename(`Votes For` = votes_for, `Votes Against` = votes_against) %>% 
  gt() %>% 
  cols_align(
    align = "center") %>% 
   tab_header(
    title = md("This Week's Predictions"),
    subtitle = md(glue("Week {length(weeks)}"))
    ) %>% 
   tab_options(
    data_row.padding = px(3)
   )
```

Group Predictions
==========================================================================

Sidebar {.sidebar} 
-------------------------------------
#### CBS Sports

<font size="4">

This week we beat or tied `r cbs_experts_beat[[length(weeks)]]` of `r cbs_experts_total[[length(weeks)]]` CBS Sports' Experts.

For the season we are currently beating or tied with `r cbs_experts_beat_season[[length(weeks)]]` of `r cbs_experts_season_total[[length(weeks)]]` CBS Sports' Experts.
 
 </font>


#### ESPN

<font size="4">

We also beat or tied `r espn_experts_beat[[length(weeks)]]` of `r espn_experts_total[[length(weeks)]]` ESPN Experts.
 
For the season we are currently beating or tied with `r espn_experts_beat_season[[length(weeks)]]` of `r espn_experts_season_total[[length(weeks)]]` ESPN Experts.

</font>

Row
--------------------------------------

### Win percentage for the week

```{r}
inst_rate <- weekly_win_percentage[[length(weekly_win_percentage)]]*100
gauge(inst_rate, min = 0, max = 100, symbol = '%', gaugeSectors(
  success = c(55, 100), warning = c(40, 54), danger = c(0, 39)
))
```

### Season Win Percentage

```{r}
inst_season <- season_win_percentage*100
gauge(inst_season, min = 0, max = 100, symbol = '%', gaugeSectors(
  success = c(55, 100), warning = c(40, 54), danger = c(0, 39)
))
```

### Games Correct
```{r}
valueBox(value = season_wins,icon = "fa-trophy",caption = "Correct Games this Season")
```

### Games Picked
```{r}
valueBox(value = season_games,icon = "fa-clipboard-list",caption = "Games Picked this Season")
```

### Number of predictions
```{r}
valueBox(value = Total,icon = "fa-users",caption = "Predictions this week")
```

Row
--------------------------------------

### 

```{r}
inst_group_table
```

### 

```{r}
ggplotly(inst_group_season_plot) %>% 
  layout(title = list(y = .93, xref = "plot"),
         margin = list(t = 40))
```

Individual Predictions
==========================================================================


Sidebar {.sidebar} 
-------------------------------------

#### Best Picks of the Week.

<font size="4">

 `r indiv_winners`
 
 </font>
 
#### Best Season Correct Percentage
<font size="4">

`r indiv_season`
 
 </font>

#### Best Adjusted Season Correct Percentage
<font size="4">

`r indiv_season_adj`

 * Adjusted season percentage accounts for the number of weeks picked.
 
 </font>

row {.tabset}
--------------------------------------

### Individual Table
```{r}
indiv_table
```

<!--
### Individual Table2

```{r, out.height="100%"}
indiv_table_2
```

-->

<!--

### Individual Plots
```{r, out.width="100%"}
#ggplotly(inst_indiv_plots)
```

-->

### Season Leaderboard
```{r, out.width="100%"}
season_leaderboard
```

### Adjusted Season Leaderboard
```{r, out.width="100%"}
adj_season_leaderboard
```

Data
==========================================================================

```{r}
datatable(
  inst.data, extensions = 'Buttons', options = list(
    dom = 'Blfrtip',
    buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
    lengthMenue = list( c(10, 25, 50, 100, -1), c(10, 25, 50, 100, "All") )
  )
)
```